A Survey on Deep Domain Adaptation in Medical Image Segmentation

PhD Qualifying Examination
Title: "A Survey on Deep Domain Adaptation in Medical Image Segmentation"
by
Mr. Zengqiang YAN
Abstract:
Despite the advancement of deep learning in automatic medical image
segmentation, performance would significantly degrade when applying the trained
deep models to new data acquired from different machines or institutions than
the training data. One straightforward way is to generate manual annotations
for every new target domain and train new models, which is costly and
time-consuming. The variations in multi-center data in medical image analysis
have brought the necessity of domain adaptation.
The aim of this survey is to give an overview of deep domain adaptation with a
specific task on medical image segmentation. After a general motivation, we
first formulate the domain adaptation and the transfer learning problems.
Second, we overview the current deep domain adaptation frameworks for medical
image segmentation. More specifically, the current deep domain adaptation
algorithms are classified into three categories namely supervised,
semi-supervised and unsupervised methods, based on the availability of manual
annotations of the target domain. Finally, we discuss several potential
research directions in deep domain adaptation for medical image segmentation.
Different from the current deep domain adaptation frameworks for medical image
segmentation, we propose a weakly supervised domain adaptation framework by
introducing additional constraints.
Keywords: Domain Adaptation, Adversarial Learning, Medical Image Segmentation
Date: Monday, 10 September 2018
Time: 4:00pm - 6:00pm
Venue: Room 4475
Lifts 25/26
Committee Members: Prof. Tim Cheng (Supervisor)
Prof. Albert Chung (Chairperson)
Dr. Pedro Sander
Prof. Weichuan Yu (ECE)
**** ALL are Welcome ****